In a communication system, the bandwidth is often a primary resource. In order to support concurrent access by numerous users in a network, this finite and expensive resource must be shared among many independent contending users. Multi-access protocols control this access of the resource among users to achieve its efficient utilization, satisfy connectivity requirements and resolve any conflict among the contending users. Many optimization problems arise in designing a multi-access protocol. Among these, there is a class of optimization problems known as NP-complete, and no polynomial algorithm can possibly solve them. Conventional methods may not be efficient arid often produce poor solutions. In this dissertation, we propose a neural network-based algorithm for solving NP-complete problems encountered in multi-access communication systems. Three combinatorial optimization problems have been solved by the proposed algorithms; namely, frame pattern design in integrated TDMA communication networks, optimal broadcast scheduling in multihop packet radio networks, and optimal channel assignment in FDM A mobile communication networks. Numerical studies have shown encouraging results in searching for the global optimal solutions by using this algorithm. The determination of the related parameters regarding convergence and solution quality is investigated in this dissertation. Performance evaluations and comparisons with other algorithms have been performed
